Crystallization behaviour and lamellar thickness distribution of metallocene‐catalyzed polymer: Effect of 1‐alkene comonomer and branch length
Bibliographic record
Abstract
Abstract The effect of comonomer and branching on the melt crystallization and lamellar thickness distribution was studied for ethylene and 1‐alkene copolymers. The comonomers used in this study are 1‐hexene, 1‐octene, and 1‐decene. A notable influence of the comonomer ratio in the feed was observed on the crystallization and melting behaviour. The Ozawa and Mo models were found suitable for these copolymers. However, variation of relative crystallinity at different heating rates preferred the Mo method over the Ozawa method. The melting behaviour and lamellar thickness distribution of the copolymers were analyzed by the help of the modified Gibbs‐ Thomson equation. The activation energies (EA) for the melt crystallization were calculated using the Kissinger method. It was observed that 1‐hexene comonomer exhibits lower EA, indicating an easier crystallization process as compared to other comonomers used. Overall, crystallization was found to be more influenced by the degree of branching rather than the comonomer type.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".